{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Benchmarking Python Clustering Algorithms on 2D Data\n",
    "\n",
    "[Other notebooks]() perform a more genenral analysis of clustering algorithms; this notebook is looking at the special case of two dimensional data. Why two dimensional? The algorithms used in DBSCAN and HDBSCAN (in particular algorithms using kd-trees and ball trees) have significantly better performance and scaling in lower dimensions. I wanted to look at how scaling compares in what is effectively the best case scenario for such algorithms. As in the other notebooks we'll be looking at a number of algorithms and implementations. \n",
    "\n",
    "The implementations being test are:\n",
    "\n",
    "* [Sklearn](http://scikit-learn.org/stable/modules/clustering.html) (which implements several algorithms):\n",
    " * K-Means clustering\n",
    " * DBSCAN clustering\n",
    " * Agglomerative clustering\n",
    " * Spectral clustering\n",
    " * Affinity Propagation\n",
    "* [Scipy](http://docs.scipy.org/doc/scipy/reference/cluster.html) (which provides basic algorithms):\n",
    " * K-Means clustering\n",
    " * Agglomerative clustering\n",
    "* [Fastcluster](http://danifold.net/fastcluster.html) (which provides very fast agglomerative clustering in C++)\n",
    "* [DeBaCl](https://github.com/CoAxLab/DeBaCl) (Density Based Clustering; similar to a mix of DBSCAN and Agglomerative)\n",
    "* [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) (A robust hierarchical version of DBSCAN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import hdbscan\n",
    "import debacl\n",
    "import fastcluster\n",
    "import sklearn.cluster\n",
    "import scipy.cluster\n",
    "import sklearn.datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_context('poster')\n",
    "sns.set_palette('Paired', 10)\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can borrow the benchmarking code from other notebooks to benchmark multiple different sizes of dataset. Because some clustering algorithms have performance that can vary quite a lot depending on the exact nature of the dataset we'll also need to run several times on randomly generated datasets of each size so as to get a better idea of the average case performance.\n",
    "\n",
    "We also need to generalise over algorithms which don't necessarily all have the same API. We can resolve that by taking a clustering function, argument tuple and keywords dictionary to let us do semi-arbitrary calls (fortunately all the algorithms do at least take the dataset to cluster as the first parameter).\n",
    "\n",
    "Finally some algorithms scale poorly, and I don't want to spend forever doing clustering of random datasets so we'll cap the maximum time an algorithm can use; once it has taken longer than max time we'll just abort there and leave the remaining entries in our datasize by samples matrix unfilled.\n",
    "\n",
    "In the end this all amounts to a fairly straightforward set of nested loops (over datasizes and number of samples) with calls to sklearn to generate mock data and the clustering function inside a timer. Add in some early abort and we're done.\n",
    "\n",
    "This time around we'll also set the default dataset dimension to be two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def benchmark_algorithm(dataset_sizes, cluster_function, function_args, function_kwds,\n",
    "                        dataset_dimension=2, max_time=45, sample_size=2):\n",
    "    \n",
    "    # Initialize the result with NaNs so that any unfilled entries \n",
    "    # will be considered NULL when we convert to a pandas dataframe at the end\n",
    "    result = np.nan * np.ones((len(dataset_sizes), sample_size))\n",
    "    for index, size in enumerate(dataset_sizes):\n",
    "        dataset_n_clusters = int(np.log2(size))\n",
    "        for s in range(sample_size):\n",
    "            # Use sklearns make_blobs to generate a random dataset with specified size\n",
    "            # dimension and number of clusters\n",
    "            data, _ = sklearn.datasets.make_blobs(n_samples=size, \n",
    "                                                       n_features=dataset_dimension, \n",
    "                                                       centers=dataset_n_clusters)\n",
    "            \n",
    "            # Start the clustering with a timer\n",
    "            start_time = time.time()\n",
    "            cluster_function(data, *function_args, **function_kwds)\n",
    "            time_taken = time.time() - start_time\n",
    "            \n",
    "            # If we are taking more than max_time then abort -- we don't\n",
    "            # want to spend excessive time on slow algorithms\n",
    "            if time_taken > max_time:\n",
    "                result[index, s] = time_taken\n",
    "                return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n",
    "                                               result.flatten()]).T, columns=['x','y'])\n",
    "            else:\n",
    "                result[index, s] = time_taken\n",
    "        \n",
    "    # Return the result as a dataframe for easier handling with seaborn afterwards\n",
    "    return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n",
    "                                   result.flatten()]).T, columns=['x','y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of all ten implementations\n",
    "\n",
    "Now we need a range of dataset sizes to test out our algorithm. Since the scaling performance is wildly different over the ten implementations we're going to look at it will be beneficial to have a number of very small dataset sizes, and increasing spacing as we get larger, spanning out to 32000 datapoints to cluster (to begin with). Numpy provides convenient ways to get this done via `arange` and vector multiplication. We'll start with step sizes of 500, then shift to steps of 1000 past 3000 datapoints, and finally steps of 2000 past 6000 datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_sizes = np.hstack([np.arange(1, 6) * 500, np.arange(3,7) * 1000, np.arange(4,17) * 2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is just a matter of running all the clustering algorithms via our benchmark function to collect up all the requsite data. This could be prettier, rolled up into functions appropriately, but sometimes brute force is good enough. More importantly (for me) since this can take a significant amount of compute time, I wanted to be able to comment out algorithms that were slow or I was uninterested in easily. Which brings me to a warning for you the reader and potential user of the notebook: this next step is very expensive. We are running ten different clustering algorithms multiple times each on twenty two different dataset sizes -- and some of the clustering algorithms are slow (we are capping out at forty five seconds per run). That means that the next cell can take an hour or more to run. That doesn't mean \"Don't try this at home\" (I actually encourage you to try this out yourself and play with dataset parameters and clustering parameters) but it does mean you should be patient if you're going to!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k_means = sklearn.cluster.KMeans(10)\n",
    "k_means_data = benchmark_algorithm(dataset_sizes, k_means.fit, (), {})\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN(eps=1.25)\n",
    "dbscan_data = benchmark_algorithm(dataset_sizes, dbscan.fit, (), {})\n",
    "\n",
    "scipy_k_means_data = benchmark_algorithm(dataset_sizes, scipy.cluster.vq.kmeans, (10,), {})\n",
    "\n",
    "scipy_single_data = benchmark_algorithm(dataset_sizes, scipy.cluster.hierarchy.single, (), {})\n",
    "\n",
    "fastclust_data = benchmark_algorithm(dataset_sizes, fastcluster.single, (), {})\n",
    "\n",
    "hdbscan_ = hdbscan.HDBSCAN()\n",
    "hdbscan_data = benchmark_algorithm(dataset_sizes, hdbscan_.fit, (), {})\n",
    "\n",
    "debacl_data = benchmark_algorithm(dataset_sizes, debacl.geom_tree.geomTree, (5, 5), {'verbose':False})\n",
    "\n",
    "agglomerative = sklearn.cluster.AgglomerativeClustering(10)\n",
    "agg_data = benchmark_algorithm(dataset_sizes, agglomerative.fit, (), {}, sample_size=4)\n",
    "\n",
    "spectral = sklearn.cluster.SpectralClustering(10)\n",
    "spectral_data = benchmark_algorithm(dataset_sizes, spectral.fit, (), {}, sample_size=6)\n",
    "\n",
    "affinity_prop = sklearn.cluster.AffinityPropagation()\n",
    "ap_data = benchmark_algorithm(dataset_sizes, affinity_prop.fit, (), {}, sample_size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to plot the results so we can see what is going on. The catch is that we have several datapoints for each dataset size and ultimately we would like to try and fit a curve through all of it to get the general scaling trend. Fortunately [seaborn](http://stanford.edu/~mwaskom/software/seaborn/) comes to the rescue here by providing `regplot` which plots a regression through a dataset, supports higher order regression (we should probably use order two as most algorithms are effectively quadratic) and handles multiple datapoints for each x-value cleanly (using the `x_estimator` keyword to put a point at the mean and draw an error bar to cover the range of data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10ce76850>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leland/.conda/envs/hdbscan_dev/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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OZnV3JydW7fzbrIGyhV6Gis4EJze2r6Nsiu8ggiAhVHSmf6CExxyvxgGgTZi6SagVu5N1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i38738fExAQwPPPv0yADyRv8zJw4IM4HA6WL1/KhQsXCA8P57bbbuexx4b5dF5FRadOXUlJSeGz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\n0aIPmTp1Gvff/y9mzpyVd87VV1/O9OkWp5xyGhMnT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RLNm1S2GZ1WGsNw55Jf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DN7CuVBmKYRiGYRhmumEhcYNzsaoR3fT0j1CAAHLVaU1xu62O1CQEzB6u1iScFICyd8FKHYOV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\nrtJKDA8UuOfG+rUXjnjJcl75tudz/XfuZo3ZyjI9jzM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rQHV2MLRsHqkVz8Pz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb8abd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=hdbscan_data, order=2, label='HDBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=fastclust_data, order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=scipy_single_data, order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=debacl_data, order=2, label='DeBaCl Geom Tree', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=spectral_data, order=2, label='Sklearn Spectral', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=agg_data, order=2, label='Sklearn Agglomerative', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=ap_data, order=2, label='Sklearn Affinity Propagation', x_estimator=np.mean)\n",
    "plt.gca().axis([0, 34000, 0, 120])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few features stand out. First of all there appear to be essentially two classes of implementation. The fast implementations tend to be implementations of single linkage agglomerative clustering, K-means, and DBSCAN. The slow cases are largely from sklearn and include agglomerative clustering (in this case using Ward instead of single linkage).\n",
    "\n",
    "We really wanted to see what happens in the two dimensional case for the better scaling algorithms, so let's drop out those slow algorithms so we can scale out a little further and get a closer look at the various algorithms that managed 32000 points in under twenty seconds. There is almost undoubtedly more to learn as we get ever larger dataset sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of fast implementations\n",
    "\n",
    "Let's compare the six fastest implementations now. We can scale out a little further as well; based on the curves above it looks like we should be able to comfortably get to 60000 data points without taking much more than a minute per run. We can also note that most of these implementations weren't that noisy so we can get away with a single run per dataset size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "large_dataset_sizes = np.arange(1,16) * 4000\n",
    "\n",
    "hdbscan_prims = hdbscan.HDBSCAN(algorithm='prims_kdtree')\n",
    "large_hdbscan_prims_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                         hdbscan_prims.fit, (), {}, max_time=90, sample_size=1)\n",
    "\n",
    "hdbscan_boruvka = hdbscan.HDBSCAN(algorithm='boruvka_kdtree')\n",
    "large_hdbscan_boruvka_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                         hdbscan_boruvka.fit, (), {}, max_time=90, sample_size=1)\n",
    "\n",
    "k_means = sklearn.cluster.KMeans(10)\n",
    "large_k_means_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                         k_means.fit, (), {}, max_time=90, sample_size=1)\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN(eps=1.25, min_samples=5)\n",
    "large_dbscan_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                        dbscan.fit, (), {}, max_time=90, sample_size=1)\n",
    "\n",
    "large_fastclust_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                           fastcluster.single, (), {}, max_time=90, sample_size=1)\n",
    "\n",
    "large_scipy_k_means_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                               scipy.cluster.vq.kmeans, (10,), {}, max_time=90, sample_size=1)\n",
    "\n",
    "large_scipy_single_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                              scipy.cluster.hierarchy.single, (), {}, max_time=90, sample_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we can use seaborn to do curve fitting and plotting, exactly as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x108721210>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7+eS7+WyU25j8yOM4CsH/pajwNzlb/tsGnpv2Hl1bt+WZu4cTcZZ7NYBlG9bT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\nRdqZC1FAR6ZutLtl9PdIX5Ob5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Xf55NIlKPd5F1CE6mrD7IjFQ5kFz54BdKdOY57WroRP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\nHt5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HlnDwEIIwPXTAXzlX74WqF4rJs/S2jE9jbaoOASjVZWnoRPIdo/gcSs6Ly2fhyhbe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\ntu/kPhN+hXPmvu7zOBs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108715310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_hdbscan_boruvka_data, order=2, label='HDBSCAN Boruvka', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_fastclust_data, order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_single_data, order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "#sns.regplot(x='x', y='y', data=large_hdbscan_prims_data, order=2, label='HDBSCAN Prims', x_estimator=np.mean)\n",
    "\n",
    "plt.gca().axis([0, 64000, 0, 150])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Fastest Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly something has gone woefully wrong with the curve fitting for the two single linkage algorithms, but what exactly? If we look at the raw data we can see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24000</td>\n",
       "      <td>7.506189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28000</td>\n",
       "      <td>10.821918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32000</td>\n",
       "      <td>22.282744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36000</td>\n",
       "      <td>26.050475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40000</td>\n",
       "      <td>212.501709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>52000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>56000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x           y\n",
       "5   24000    7.506189\n",
       "6   28000   10.821918\n",
       "7   32000   22.282744\n",
       "8   36000   26.050475\n",
       "9   40000  212.501709\n",
       "10  44000         NaN\n",
       "11  48000         NaN\n",
       "12  52000         NaN\n",
       "13  56000         NaN\n",
       "14  60000         NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_fastclust_data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24000</td>\n",
       "      <td>10.324891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28000</td>\n",
       "      <td>15.216605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32000</td>\n",
       "      <td>21.523087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36000</td>\n",
       "      <td>29.564827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40000</td>\n",
       "      <td>174.514488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>52000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>56000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x           y\n",
       "5   24000   10.324891\n",
       "6   28000   15.216605\n",
       "7   32000   21.523087\n",
       "8   36000   29.564827\n",
       "9   40000  174.514488\n",
       "10  44000         NaN\n",
       "11  48000         NaN\n",
       "12  52000         NaN\n",
       "13  56000         NaN\n",
       "14  60000         NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_scipy_single_data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that at around 44000 points we hit a wall and the runtimes spiked. A hint is that I'm running this on a laptop with 8GB of RAM. Both single linkage algorithms use `scipy.spatial.pdist` to compute pairwise distances between points, which returns an array of shape `(n(n-1)/2, 1)` of doubles. A quick computation shows that that array of distances is quite large once we nave 44000 points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.211998105049133"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_of_array = 44000 * (44000 - 1) / 2         # from pdist documentation\n",
    "bytes_in_array = size_of_array * 8              # Since doubles use 8 bytes\n",
    "gigabytes_used = bytes_in_array / (1024.0 ** 3)  # divide out to get the number of GB\n",
    "gigabytes_used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assume that my laptop is keeping much other than that distance array in RAM then clearly we are going to spend time paging out the distance array to disk and back and hence we will see the runtimes increase dramatically as we become disk IO bound. \n",
    "\n",
    "For fastcluster there is an alternative `linkage_vector` function that can be used if you are doing single linkage and have the original data (as opposed to a matrix of dissimilarities). We'll rerun fastcluster with this instead.\n",
    "\n",
    "For scipy single linkahe if we just leave off the last element we can get a better idea of the curves, but keep in mind that this single linkage implemtation does not scale past a limit set by your available RAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "large_fastclust_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                           fastcluster.linkage_vector, (), {}, max_time=90, sample_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to replot with the new fastcluster data, and the truncated scipy single linkage data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x104401d50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2cDZt+haAhIQEBgzowx9//M6AAYN5772ZNGnSjOXLl/L115+7rGvs2LepUKEi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\n3/jgg940buxH586vsXPn18lef/16IO3avcqIEYNs7/fChXOMGDGIFi1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IIYR4Dum6jn49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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0ea550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_hdbscan_boruvka_data, order=2, label='HDBSCAN Boruvka', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_fastclust_data, order=2, label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_single_data[:8], order=2, label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "#sns.regplot(x='x', y='y', data=large_hdbscan_prims_data, order=2, label='HDBSCAN Prims', x_estimator=np.mean)\n",
    "\n",
    "plt.gca().axis([0, 64000, 0, 150])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Fastest Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This rules out scipy single linkage as beign in the same class as the others even igf it could scale. Even fastcluster is starting to show the effects of having an $O(n^2)$ algorithm, despite having worked very hard to improve constants.\n",
    "\n",
    "In practice this is going to mean that for larger datasets you are going to be very constrained in what algorithms you can apply: if you get enough datapoints only K-Means, DBSCAN and HDBSCAN will be left. Let's get a closer look at the performance of these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of K-Means and DBSCAN  and HDBSCAN implementations\n",
    "\n",
    "At this point we can scale out to 300000 datapoints easily enough: These algorithms can use various data structures to avoid having to compute the full pairwise distance matrix and thus potentially have much more favourable asymptotic complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "huge_dataset_sizes = np.arange(1,16) * 20000\n",
    "\n",
    "k_means = sklearn.cluster.KMeans(10)\n",
    "huge_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                        k_means.fit, (), {}, max_time=120, sample_size=2)\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN()\n",
    "huge_dbscan_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                       dbscan.fit, (), {}, max_time=240, sample_size=2)\n",
    "\n",
    "huge_scipy_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                              scipy.cluster.vq.kmeans, (10,), {}, max_time=240, sample_size=2)\n",
    "\n",
    "hdbscan_boruvka = hdbscan.HDBSCAN(algorithm='boruvka_kdtree')\n",
    "huge_hdbscan_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                       hdbscan_boruvka.fit, (), {}, max_time=240, sample_size=4)\n",
    "\n",
    "huge_fastcluster_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                            fastcluster.linkage_vector, (), {}, max_time=240, sample_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10cf59d90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LtiZMeJ2KFR9k0qR3qV27LnPnzmbXru8A2Lt3LxMnvkmzZs2ZOnUmXbo8x+ef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\nFdv4/iObaO8bvXlbJpXiwjCXV52xiNmNteO6/1BgaKnPMqexltaW+n3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10c38+PEt7OjJ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6WaR67scf12ga/cyvL6fJZra4WevcuGom2FMOlafOLKsIiFA6K1JlvdaNiL5guzjS5G65W1vp7L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\nVxzMzMzMzKwlVxzMzMzMzKyl/weR1tJqdbWYwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0e1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=huge_k_means_data, order=2, label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_dbscan_data, order=2, label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_scipy_k_means_data, order=2, label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_hdbscan_data, order=2, label='HDBSCAN', x_estimator=np.mean)\n",
    "#sns.regplot(x='x', y='y', data=huge_fastcluster_data, order=2, label='Fastcluster', x_estimator=np.mean)\n",
    "\n",
    "\n",
    "plt.gca().axis([0, 310000, 0, 60])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of K-Means and DBSCAN')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While in [other notebooks]() the two K-means implementations separated out, with the Sklearn implementation being clearly better, here we see they very much keep pace. Also, interestingly we see the full benefits of the dual tree Boruvka algorithm for minimum spanning trees, with the HDBSCAN implementation actually outperforming ordinary DBSCAN for large dataset sizes. In higher dimensions this won't be the case, but it makes it clear that if you have very low dimensional data then HDBSCAN (from which you can extract ordinary DBSCAN results if you so desire) is clearly the better choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But should I get a coffee?\n",
    "\n",
    "So we know which implementations scale and which don't; a more useful thing to know in practice is, given a dataset, what can I run interactively? What can I run while I go and grab some coffee? How about a run over lunch? What if I'm willing to wait until I get in tomorrow morning? Each of these represent significant breaks in productivity -- once you aren't working interactively anymore your productivity drops measurably, and so on.\n",
    "\n",
    "We can build a table for this. To start we'll need to be able to approximate how long a given clustering implementation will take to run. Fortunately we already gathered a lot of that data; if we load up the `statsmodels` package we can fit the data (with a quadratic or $n\\log n$ fit depending on the implementation) and use the resulting model to make our predictions. Obviously this has some caveats: if you fill your RAM with a distance matrix your runtime isn't going to fit the curve.\n",
    "\n",
    "I've hand built a `time_samples` list to give a reasonable set of potential data sizes that are nice and human readable. After that we just need a function to fit and build the curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "\n",
    "time_samples = [1000, 2000, 5000, 10000, 25000, 50000, 75000, 100000, 250000, 500000, 750000,\n",
    "               1000000, 2500000, 5000000, 10000000, 50000000, 100000000, 500000000, 1000000000]\n",
    "\n",
    "def get_timing_series(data, quadratic=True):\n",
    "    if quadratic:\n",
    "        data['x_squared'] = data.x**2\n",
    "        model = sm.ols('y ~ x + x_squared', data=data).fit()\n",
    "        predictions = [model.params.dot([1.0, i, i**2]) for i in time_samples]\n",
    "        return pd.Series(predictions, index=pd.Index(time_samples))\n",
    "    else: # assume n log(n)\n",
    "        data['xlogx'] = data.x * np.log(data.x)\n",
    "        model = sm.ols('y ~ x + xlogx', data=data).fit()\n",
    "        predictions = [model.params.dot([1.0, i, i*np.log(i)]) for i in time_samples]\n",
    "        return pd.Series(predictions, index=pd.Index(time_samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we run that for each of our pre-existing datasets to extrapolate out predicted performance on the relevant dataset sizes. A little pandas wrangling later and we've produced a table of roughly how large a dataset you can tackle in each time frame with each implementation. I had to leave out the scipy KMeans timings because the noise in timing results caused the model to be unrealistic at larger data sizes. Note how the $O(n\\log n)$ algorithms utterly dominate here, and HDBSCAN achieves that complexity with the dual tree Boruvka on two dimensional data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Get Coffee</th>\n",
       "      <th>Over Lunch</th>\n",
       "      <th>Overnight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AffinityPropagation</th>\n",
       "      <td>2000</td>\n",
       "      <td>5000</td>\n",
       "      <td>25000</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectral</th>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>50000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agglomerative</th>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>25000</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeBaCl</th>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>75000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScipySingleLinkage</th>\n",
       "      <td>25000</td>\n",
       "      <td>50000</td>\n",
       "      <td>100000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastcluster</th>\n",
       "      <td>100000</td>\n",
       "      <td>250000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDBSCAN</th>\n",
       "      <td>500000</td>\n",
       "      <td>2500000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>100000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBSCAN</th>\n",
       "      <td>250000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKLearn KMeans</th>\n",
       "      <td>750000</td>\n",
       "      <td>5000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Interactive  Get Coffee  Over Lunch  Overnight\n",
       "AffinityPropagation         2000        5000       25000      75000\n",
       "Spectral                    5000       10000       50000     100000\n",
       "Agglomerative               2000       10000       25000      75000\n",
       "DeBaCl                      5000       10000       75000     100000\n",
       "ScipySingleLinkage         25000       50000      100000     250000\n",
       "Fastcluster               100000      250000     1000000    2500000\n",
       "HDBSCAN                   500000     2500000    10000000  100000000\n",
       "DBSCAN                    250000     1000000    10000000   50000000\n",
       "SKLearn KMeans            750000     5000000    10000000   50000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap_timings = get_timing_series(ap_data)\n",
    "spectral_timings = get_timing_series(spectral_data)\n",
    "agg_timings = get_timing_series(agg_data)\n",
    "debacl_timings = get_timing_series(debacl_data)\n",
    "fastclust_timings = get_timing_series(large_fastclust_data.ix[:10,:].copy())\n",
    "scipy_single_timings = get_timing_series(large_scipy_single_data.ix[:10,:].copy())\n",
    "hdbscan_boruvka = get_timing_series(huge_hdbscan_data, quadratic=False)\n",
    "#scipy_k_means_timings = get_timing_series(huge_scipy_k_means_data, quadratic=False)\n",
    "dbscan_timings = get_timing_series(huge_dbscan_data, quadratic=False)\n",
    "k_means_timings = get_timing_series(huge_k_means_data, quadratic=True)\n",
    "\n",
    "timing_data = pd.concat([ap_timings, spectral_timings, agg_timings, debacl_timings, \n",
    "                            scipy_single_timings, fastclust_timings, hdbscan_boruvka, \n",
    "                            dbscan_timings, k_means_timings\n",
    "                           ], axis=1)\n",
    "timing_data.columns=['AffinityPropagation', 'Spectral', 'Agglomerative',\n",
    "                                       'DeBaCl', 'ScipySingleLinkage', 'Fastcluster',\n",
    "                                       'HDBSCAN', 'DBSCAN', 'SKLearn KMeans'\n",
    "                                      ]\n",
    "def get_size(series, max_time):\n",
    "    return series.index[series < max_time].max()\n",
    "\n",
    "datasize_table = pd.concat([\n",
    "                            timing_data.apply(get_size, max_time=30),\n",
    "                            timing_data.apply(get_size, max_time=300),\n",
    "                            timing_data.apply(get_size, max_time=3600),\n",
    "                            timing_data.apply(get_size, max_time=8*3600)\n",
    "                            ], axis=1)\n",
    "datasize_table.columns=('Interactive', 'Get Coffee', 'Over Lunch', 'Overnight')\n",
    "datasize_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "If you have two dimensional data then HDBSCN is an excellent choice, outperforming regular DBSCAN and not being too far behind K-Means."
   ]
  }
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